from joblib import load
import numpy as np


class Classification(object):
    def __init__(self):
        self.knn = load('knn_classifier.joblib')
        self.scaler = load('feature_scaler.joblib')
        self.le = load('label_encoder.joblib')

    def get_predicter_category(self, new_data):
        """获取分类结果"""

        # 转换为数组并保存特定特征顺序
        featrue_order = [
            'eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps',
            'grossprofit_margin', 'npta'
        ]
        new_values = np.array([[new_data[col] for col in featrue_order]])

        # 标准化新数据
        new_scaled = self.scaler.transform(new_values)

        # 预测分类
        predicted_label = self.knn.predict(new_scaled)
        predicted_category = self.le.inverse_transform(predicted_label)

        return predicted_category[0]


if __name__ == '__main__':
    ci = Classification()
    new_data = {
        'eps': "-0.8309",
        'total_revenue_ps': "11.9673",
        'undist_profit_ps': "7.0209",
        'gross_margin': "11586700000",
        'fcff': "-12879100000",
        'fcfe': "-5263460000",
        'tangible_asset': "17987300000",
        'bps': "20.2568",
        'grossprofit_margin': "8.1152",
        'npta': "-0.5821"
    }
    predicted_category = ci.get_predicter_category(new_data)
    print(predicted_category)